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rufimelo
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Legal-BERTimbau-large

Fill-Mask
Transformers
PyTorch
Portuguese
bert
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xet
Community
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@@ -25,16 +25,16 @@ The performance of Language Models can change drastically when there is a domain
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  | Model | Arch. | #Layers | #Params |
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  | ---------------------------------------- | ---------- | ------- | ------- |
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- | `rufimelo/Legal_BERTimbau` | BERT-Large | 24 | 335M |
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  ## Usage
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  ```python
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  from transformers import AutoTokenizer, AutoModelForMaskedLM
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- tokenizer = AutoTokenizer.from_pretrained("rufimelo/Legal_BERTimbau")
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- model = AutoModelForMaskedLM.from_pretrained("rufimelo/Legal_BERTimbau")
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  ```
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  ### Masked language modeling prediction example
@@ -43,8 +43,8 @@ model = AutoModelForMaskedLM.from_pretrained("rufimelo/Legal_BERTimbau")
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  from transformers import pipeline
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  from transformers import AutoTokenizer, AutoModelForMaskedLM
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- tokenizer = AutoTokenizer.from_pretrained("rufimelo/Legal_BERTimbau")
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- model = AutoModelForMaskedLM.from_pretrained("rufimelo/Legal_BERTimbau")
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  pipe = pipeline('fill-mask', model=model, tokenizer=tokenizer)
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  pipe('O advogado apresentou [MASK] para o juíz')
@@ -77,7 +77,7 @@ pipe('O advogado apresentou [MASK] para o juíz')
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  import torch
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  from transformers import AutoModel
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- model = AutoModel.from_pretrained('rufimelo/Legal_BERTimbau')
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  input_ids = tokenizer.encode('O advogado apresentou recurso para o juíz', return_tensors='pt')
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  with torch.no_grad():
 
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  | Model | Arch. | #Layers | #Params |
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  | ---------------------------------------- | ---------- | ------- | ------- |
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+ | `rufimelo/Legal-BERTimbau-large` | BERT-Large | 24 | 335M |
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  ## Usage
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  ```python
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  from transformers import AutoTokenizer, AutoModelForMaskedLM
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+ tokenizer = AutoTokenizer.from_pretrained("rufimelo/Legal-BERTimbau-large")
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+ model = AutoModelForMaskedLM.from_pretrained("rufimelo/Legal-BERTimbau-large")
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  ```
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  ### Masked language modeling prediction example
 
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  from transformers import pipeline
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  from transformers import AutoTokenizer, AutoModelForMaskedLM
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+ tokenizer = AutoTokenizer.from_pretrained("rufimelo/Legal-BERTimbau-large")
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+ model = AutoModelForMaskedLM.from_pretrained("rufimelo/Legal-BERTimbau-large")
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  pipe = pipeline('fill-mask', model=model, tokenizer=tokenizer)
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  pipe('O advogado apresentou [MASK] para o juíz')
 
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  import torch
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  from transformers import AutoModel
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+ model = AutoModel.from_pretrained('rufimelo/Legal-BERTimbau-large')
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  input_ids = tokenizer.encode('O advogado apresentou recurso para o juíz', return_tensors='pt')
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  with torch.no_grad():